Fault Detection Technique for Hierarchical Monitoring of Steel Making Facilities Based on Data Science
Takehide Hirata, Masafumi Matsushita, Yukinori Iizuka, Noritsugu Suzuki
In steel making processes, influence of an equipment fault on production operation is significant. It is strongly required to detect an equipment fault at an early stage and to prevent the damage. Therefore, fault detection technique for steel making facilities based on data science is developed as an online-monitoring system. One of main features of the developed system is hierarchical monitoring consisting of three levels such as an entire process, facilities and sensors. Another is display of heat-mapping according to the degree of anomaly for huge number of monitoring items. Some anomaly signs at the hot rolling process where the system has been developed are successfully detected.
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